A key challenge in inventory management is to identify policies that optimally replenish from multiple suppliers. To solve such optimization problems, managers need decide what quantities order each supplier, given the net and outstanding orders, so expected backlogging, holding, sourcing costs are jointly minimized. Inventory problems have been studied extensively for over 60 years, yet even b...

We consider a production-inventory system where the production and demand follow Poisson processes whose rates are modulated by an external stochastic environment process. We study two such systems: with and without an external supplier, and show an interesting stochastic decomposition relationship between the steady state distribution of inventory levels in these two systems. We consider the o...

Large multi-echelon inventory systems usually consist of hundreds of thousands of stock keep units (SKU). Calculating inventory policies for each product is a computational burden that necessitates the need for more efficient policy setting techniques that reduce computational time and increases managerial convenience. The main objective of our research is to investigate the effect of segmentat...

In an assemble-to-order system, a wide variety of products are rapidly assembled from component inventories in response to customer orders. We assume that orders must be filled within a product-specific target lead time. In the event that some of the components required to fill an order are out of stock, these components must be expedited. The objective is to minimize the expected infinite-hori...

This paper is concerned with the development of an intelligent inventory management system which aims at bridging the substantial gap between the theory and the practice of inventory management. The proposed system attempts to achieve this by providing automatic demand and lead time pattern identification and model selection facilities. The process of demand pattern identification together with...

Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limi...